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            <td width="10%" class="headerItem">Current view:</td>
            <td width="35%" class="headerValue"><a href="../../../index.html">top level</a> - <a href="index.html">src/caffe/layers</a> - deconv_layer.cpp<span style="font-size: 80%;"> (source / <a href="deconv_layer.cpp.func-sort-c.html">functions</a>)</span></td>
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            <td width="10%" class="headerCovTableHead">Hit</td>
            <td width="10%" class="headerCovTableHead">Total</td>
            <td width="15%" class="headerCovTableHead">Coverage</td>
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            <td class="headerItem">Test:</td>
            <td class="headerValue">code analysis</td>
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            <td class="headerItem">Lines:</td>
            <td class="headerCovTableEntry">1</td>
            <td class="headerCovTableEntry">46</td>
            <td class="headerCovTableEntryLo">2.2 %</td>
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            <td class="headerItem">Date:</td>
            <td class="headerValue">2020-09-11 22:50:33</td>
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            <td class="headerItem">Functions:</td>
            <td class="headerCovTableEntry">1</td>
            <td class="headerCovTableEntry">11</td>
            <td class="headerCovTableEntryLo">9.1 %</td>
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            <td class="headerItem">Legend:</td>
            <td class="headerValueLeg">            Lines:
            <span class="coverLegendCov">hit</span>
            <span class="coverLegendNoCov">not hit</span>
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<pre class="sourceHeading">          Line data    Source code</pre>
<pre class="source">
<a name="1"><span class="lineNum">       1 </span>            : #include &lt;vector&gt;</a>
<span class="lineNum">       2 </span>            : 
<span class="lineNum">       3 </span>            : #include &quot;caffe/layers/deconv_layer.hpp&quot;
<span class="lineNum">       4 </span>            : 
<span class="lineNum">       5 </span>            : namespace caffe {
<a name="6"><span class="lineNum">       6 </span>            : </a>
<span class="lineNum">       7 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">       8 </span><span class="lineNoCov">          0 : void DeconvolutionLayer&lt;Dtype&gt;::compute_output_shape() {</span>
<span class="lineNum">       9 </span><span class="lineNoCov">          0 :   const int* kernel_shape_data = this-&gt;kernel_shape_.cpu_data();</span>
<span class="lineNum">      10 </span><span class="lineNoCov">          0 :   const int* stride_data = this-&gt;stride_.cpu_data();</span>
<span class="lineNum">      11 </span><span class="lineNoCov">          0 :   const int* pad_data = this-&gt;pad_.cpu_data();</span>
<span class="lineNum">      12 </span><span class="lineNoCov">          0 :   const int* dilation_data = this-&gt;dilation_.cpu_data();</span>
<span class="lineNum">      13 </span>            :   this-&gt;output_shape_.clear();
<span class="lineNum">      14 </span><span class="lineNoCov">          0 :   for (int i = 0; i &lt; this-&gt;num_spatial_axes_; ++i) {</span>
<span class="lineNum">      15 </span>            :     // i + 1 to skip channel axis
<span class="lineNum">      16 </span><span class="lineNoCov">          0 :     const int input_dim = this-&gt;input_shape(i + 1);</span>
<span class="lineNum">      17 </span><span class="lineNoCov">          0 :     const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;</span>
<span class="lineNum">      18 </span><span class="lineNoCov">          0 :     const int output_dim = stride_data[i] * (input_dim - 1)</span>
<span class="lineNum">      19 </span><span class="lineNoCov">          0 :         + kernel_extent - 2 * pad_data[i];</span>
<span class="lineNum">      20 </span><span class="lineNoCov">          0 :     this-&gt;output_shape_.push_back(output_dim);</span>
<span class="lineNum">      21 </span>            :   }
<span class="lineNum">      22 </span><span class="lineNoCov">          0 : }</span>
<a name="23"><span class="lineNum">      23 </span>            : </a>
<span class="lineNum">      24 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      25 </span><span class="lineNoCov">          0 : void DeconvolutionLayer&lt;Dtype&gt;::Forward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom,</span>
<span class="lineNum">      26 </span>            :       const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top) {
<span class="lineNum">      27 </span><span class="lineNoCov">          0 :   const Dtype* weight = this-&gt;blobs_[0]-&gt;cpu_data();</span>
<span class="lineNum">      28 </span><span class="lineNoCov">          0 :   for (int i = 0; i &lt; bottom.size(); ++i) {</span>
<span class="lineNum">      29 </span><span class="lineNoCov">          0 :     const Dtype* bottom_data = bottom[i]-&gt;cpu_data();</span>
<span class="lineNum">      30 </span><span class="lineNoCov">          0 :     Dtype* top_data = top[i]-&gt;mutable_cpu_data();</span>
<span class="lineNum">      31 </span><span class="lineNoCov">          0 :     for (int n = 0; n &lt; this-&gt;num_; ++n) {</span>
<span class="lineNum">      32 </span><span class="lineNoCov">          0 :       this-&gt;backward_cpu_gemm(bottom_data + n * this-&gt;bottom_dim_, weight,</span>
<span class="lineNum">      33 </span><span class="lineNoCov">          0 :           top_data + n * this-&gt;top_dim_);</span>
<span class="lineNum">      34 </span><span class="lineNoCov">          0 :       if (this-&gt;bias_term_) {</span>
<span class="lineNum">      35 </span><span class="lineNoCov">          0 :         const Dtype* bias = this-&gt;blobs_[1]-&gt;cpu_data();</span>
<span class="lineNum">      36 </span><span class="lineNoCov">          0 :         this-&gt;forward_cpu_bias(top_data + n * this-&gt;top_dim_, bias);</span>
<span class="lineNum">      37 </span>            :       }
<span class="lineNum">      38 </span>            :     }
<span class="lineNum">      39 </span>            :   }
<span class="lineNum">      40 </span><span class="lineNoCov">          0 : }</span>
<a name="41"><span class="lineNum">      41 </span>            : </a>
<span class="lineNum">      42 </span>            : template &lt;typename Dtype&gt;
<span class="lineNum">      43 </span><span class="lineNoCov">          0 : void DeconvolutionLayer&lt;Dtype&gt;::Backward_cpu(const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; top,</span>
<span class="lineNum">      44 </span>            :       const vector&lt;bool&gt;&amp; propagate_down, const vector&lt;Blob&lt;Dtype&gt;*&gt;&amp; bottom) {
<span class="lineNum">      45 </span><span class="lineNoCov">          0 :   const Dtype* weight = this-&gt;blobs_[0]-&gt;cpu_data();</span>
<span class="lineNum">      46 </span><span class="lineNoCov">          0 :   Dtype* weight_diff = this-&gt;blobs_[0]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">      47 </span><span class="lineNoCov">          0 :   for (int i = 0; i &lt; top.size(); ++i) {</span>
<span class="lineNum">      48 </span><span class="lineNoCov">          0 :     const Dtype* top_diff = top[i]-&gt;cpu_diff();</span>
<span class="lineNum">      49 </span><span class="lineNoCov">          0 :     const Dtype* bottom_data = bottom[i]-&gt;cpu_data();</span>
<span class="lineNum">      50 </span><span class="lineNoCov">          0 :     Dtype* bottom_diff = bottom[i]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">      51 </span>            :     // Bias gradient, if necessary.
<span class="lineNum">      52 </span><span class="lineNoCov">          0 :     if (this-&gt;bias_term_ &amp;&amp; this-&gt;param_propagate_down_[1]) {</span>
<span class="lineNum">      53 </span><span class="lineNoCov">          0 :       Dtype* bias_diff = this-&gt;blobs_[1]-&gt;mutable_cpu_diff();</span>
<span class="lineNum">      54 </span><span class="lineNoCov">          0 :       for (int n = 0; n &lt; this-&gt;num_; ++n) {</span>
<span class="lineNum">      55 </span><span class="lineNoCov">          0 :         this-&gt;backward_cpu_bias(bias_diff, top_diff + n * this-&gt;top_dim_);</span>
<span class="lineNum">      56 </span>            :       }
<span class="lineNum">      57 </span>            :     }
<span class="lineNum">      58 </span><span class="lineNoCov">          0 :     if (this-&gt;param_propagate_down_[0] || propagate_down[i]) {</span>
<span class="lineNum">      59 </span><span class="lineNoCov">          0 :       for (int n = 0; n &lt; this-&gt;num_; ++n) {</span>
<span class="lineNum">      60 </span>            :         // Gradient w.r.t. weight. Note that we will accumulate diffs.
<span class="lineNum">      61 </span><span class="lineNoCov">          0 :         if (this-&gt;param_propagate_down_[0]) {</span>
<span class="lineNum">      62 </span><span class="lineNoCov">          0 :           this-&gt;weight_cpu_gemm(top_diff + n * this-&gt;top_dim_,</span>
<span class="lineNum">      63 </span><span class="lineNoCov">          0 :               bottom_data + n * this-&gt;bottom_dim_, weight_diff);</span>
<span class="lineNum">      64 </span>            :         }
<span class="lineNum">      65 </span>            :         // Gradient w.r.t. bottom data, if necessary, reusing the column buffer
<span class="lineNum">      66 </span>            :         // we might have just computed above.
<span class="lineNum">      67 </span><span class="lineNoCov">          0 :         if (propagate_down[i]) {</span>
<span class="lineNum">      68 </span><span class="lineNoCov">          0 :           this-&gt;forward_cpu_gemm(top_diff + n * this-&gt;top_dim_, weight,</span>
<span class="lineNum">      69 </span><span class="lineNoCov">          0 :               bottom_diff + n * this-&gt;bottom_dim_,</span>
<span class="lineNum">      70 </span>            :               this-&gt;param_propagate_down_[0]);
<span class="lineNum">      71 </span>            :         }
<span class="lineNum">      72 </span>            :       }
<span class="lineNum">      73 </span>            :     }
<span class="lineNum">      74 </span>            :   }
<span class="lineNum">      75 </span><span class="lineNoCov">          0 : }</span>
<a name="76"><span class="lineNum">      76 </span>            : </a>
<span class="lineNum">      77 </span>            : #ifdef CPU_ONLY
<span class="lineNum">      78 </span><span class="lineNoCov">          0 : STUB_GPU(DeconvolutionLayer);</span>
<span class="lineNum">      79 </span>            : #endif
<span class="lineNum">      80 </span>            : 
<a name="81"><span class="lineNum">      81 </span>            : INSTANTIATE_CLASS(DeconvolutionLayer);</a>
<span class="lineNum">      82 </span>            : 
<span class="lineNum">      83 </span><span class="lineCov">          2 : }  // namespace caffe</span>
</pre>
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